# DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features

arXiv: https://arxiv.org/abs/2008.05416 · HTML: https://mlsanity.com/paper/2008.05416/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness](https://mlsanity.com/paper/2107.07707/) (2021)
- [MASt3R-Fusion: Integrating Feed-Forward Visual Model with IMU, GNSS for High-Functionality SLAM](https://mlsanity.com/paper/2509.20757/) (2025)
- [DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping](https://mlsanity.com/paper/2403.13714/) (2024)
- [RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments](https://mlsanity.com/paper/2207.03539/) (2022)
- [Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines](https://mlsanity.com/paper/2503.02955/) (2025)
- [Topological RANSAC for instance verification and retrieval without fine-tuning](https://mlsanity.com/paper/2310.06486/) (2023)
- [A Collaborative Visual SLAM Framework for Service Robots](https://mlsanity.com/paper/2102.03228/) (2021)
- [SLAM for Visually Impaired People: a Survey](https://mlsanity.com/paper/2212.04745/) (2022)
- [DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing](https://mlsanity.com/paper/2401.09160/) (2024)
- [Monocular Localization with Semantics Map for Autonomous Vehicles](https://mlsanity.com/paper/2406.03835/) (2024)
- [ViBA: Implicit Bundle Adjustment with Geometric and Temporal Consistency for Robust Visual Matching](https://mlsanity.com/paper/2604.03377/) (2026)
- [Black-box Adversarial Attacks on CNN-based SLAM Algorithms](https://mlsanity.com/paper/2505.24654/) (2025)
- [DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM](https://mlsanity.com/paper/2607.01757/) (2026)
- [Misanthrope: A Privacy-Preserving Keypoint Detector](https://mlsanity.com/paper/2608.23012/) (2026)
- [CGS-SLAM: Collaborative Gaussian Splatting based SLAM for Multi-Agent Reconstruction](https://mlsanity.com/paper/2608.26868/) (2026)

## Abstract (arXiv)

A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments. This paper shows that feature extraction with deep convolutional neural networks (CNNs) can be seamlessly incorporated into a modern SLAM framework. The proposed SLAM system utilizes a state-of-the-art CNN to detect keypoints in each image frame, and to give not only keypoint descriptors, but also a global descriptor of the whole image. These local and global features are then used by different SLAM modules, resulting in much more robustness against environmental changes and viewpoint changes compared with using hand-crafted features. We also train a visual vocabulary of local features with a Bag of Words (BoW) method. Based on the local features, global features, and the vocabulary, a highly reliable loop closure detection method is built. Experimental results show that all the proposed modules significantly outperforms the baseline, and the full system achieves much lower trajectory errors and much higher correct rates on all evaluated data. Furthermore, by optimizing the CNN with Intel OpenVINO toolkit and utilizing the Fast BoW library, the system benefits greatly from the SIMD (single-instruction-multiple-data) techniques in modern CPUs. The full system can run in real-time without any GPU or other accelerators. The code is public at https://github.com/ivipsourcecode/dxslam.